Estimation in Weibull Distribution Under Progressively Type-I Hybrid Censored Data

TL;DR

This paper develops and compares various estimation methods, including MLE, Bayesian, and shrinkage estimators, for Weibull distribution parameters under complex censored data schemes, supported by simulations and real data analysis.

Contribution

It introduces new estimation techniques for Weibull parameters under progressive hybrid censoring, combining classical and Bayesian approaches with comprehensive performance evaluation.

Findings

MLEs are effective under certain censoring schemes.

Bayesian estimators perform well with MCMC methods.

Shrinkage pre-test estimators improve estimation accuracy.

Abstract

In this article, we consider the estimation of unknown parameters of Weibull distribution when the lifetime data are observed in the presence of progressively type-I hybrid censoring scheme. The Newton-Raphson algorithm, Expectation-Maximization (EM) algorithm and Stochastic EM (SEM) algorithm are utilized to derive the maximum likelihood estimates (MLEs) for the unknown parameters. Moreover, Bayesian estimators using Tierney-Kadane Method and Markov Chain Monte Carlo (MCMC) method are obtained under three different loss functions, namely, squared error loss (SEL), linear-exponential (LINEX) and generalized entropy loss (GEL) functions. Also, the shrinkage pre-test estimators are derived. An extensive Monte Carlo simulation experiment is conducted under different schemes so that the performances of the listed estimators are compared using mean squared error, confidence interval length and coverage probabilities. Asymptotic normality and MCMC samples are used to obtain the confidence intervals and highest posterior density (HPD) intervals respectively. Further, a real data example is presented to illustrate the methods. Finally, some conclusive remarks are presented.